🔗 Who cites «CommonsenseQA 2.0: Exposing the Limits of AI through Gamification»

87 papers of our library cite it (of the 149,439 whose bibliographies we read) · 15 with a page here, the most cited first · ← back to «CommonsenseQA 2.0: Exposing the Limits of AI through Gamification»

1. Chain-of-Thought Prompting Elicits Reasoning in Large Language ModelsarXiv:2201.11903Jason Wei, Xuezhi Wang, Dale Schuurmans, et al.2022 · 11,499 citations
2. Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLsConference on Neural Information Processing Systems (NeurIPS)Jinyang Li, Binyuan Hui, Ge Qu, et al.2023 · 433 citations
3. Symbolic Knowledge Distillation: from General Language Models to Commonsense ModelsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Peter West, Chandra Bhagavatula, Jack Hessel, et al.2022 · 349 citations
4. UL2: Unifying Language Learning ParadigmsarXiv:2205.05131Yi Tay, Mostafa Dehghani, Vinh Q. Tran, et al.2022 · 347 citations
5. WANLI: Worker and AI Collaboration for Natural Language Inference Dataset CreationarXiv:2201.05955Alisa Liu, Swabha Swayamdipta, Noah A. Smith, et al.2022 · 317 citations
6. Maieutic Prompting: Logically Consistent Reasoning with Recursive ExplanationsConference on Empirical Methods in Natural Language Processing (EMNLP)Jaehun Jung, Lianhui Qin, Sean Welleck, et al.2022 · 219 citations
7. Natural Language Reasoning, A SurveyACM Computing SurveysFei Yu, Hongbo Zhang, Prayag Tiwari, et al.2023 · 130 citations
8. Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and FuturearXiv:2309.15402Zheng Chu, Jingchang Chen, Qianglong Chen, et al.2023 · 125 citations
9. Can Machines Learn Morality? The Delphi ExperimentarXiv:2110.07574Liwei Jiang, Jena D. Hwang, Chandra Bhagavatula, et al.2021 · 110 citations
10. FLASK: Fine-grained Language Model Evaluation based on Alignment Skill SetsarXiv:2307.10928Seonghyeon Ye, Doyoung Kim, Sungdong Kim, et al.2023 · 100 citations
11. MuSR: Testing the Limits of Chain-of-thought with Multistep Soft ReasoningInternational Conference on Learning Representations (ICLR)Zayne Sprague, Xi Ye, Kaj Bostrom, et al.2023 · 74 citations
12. Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the AboveAnnual Meeting of the Association for Computational Linguistics (ACL)Nishant Balepur, Rachel Rudinger, Jordan Boyd-Graber2025 · 28 citations
13. CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and AugmentationTransactions of the Association for Computational Linguistics (TACL)Ingo Ziegler, Abdullatif Köksal, Desmond Elliott, et al.2025 · 20 citations
14. Generalizable LLM Learning of Graph Synthetic Data with Post-training AlignmentFindings of the Association for Computational LinguisticsYizhuo Zhang, Heng Wang, Shangbin Feng, et al.2026
15. Synthetic Data Generation for Training Diversified Commonsense Reasoning ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Tianhui Zhang, Bei Peng, Danushka Bollegala2026

72 more papers of our library cite it; they have no page here yet.

This page in Markdown → https://md.research.elamur.ai/free/en/papers/talmor-2022-commonsenseqa-2-0-exposing-limits/cited-by/